Strategies to save energy in the context of the energy crisis: a review

M Farghali, AI Osman, IMA Mohamed, Z Chen… - Environmental …, 2023 - Springer
New technologies, systems, societal organization and policies for energy saving are
urgently needed in the context of accelerated climate change, the Ukraine conflict and the …

Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities

T Ahmad, D Zhang, C Huang, H Zhang, N Dai… - Journal of Cleaner …, 2021 - Elsevier
The energy industry is at a crossroads. Digital technological developments have the
potential to change our energy supply, trade, and consumption dramatically. The new …

CNN-LSTM: An efficient hybrid deep learning architecture for predicting short-term photovoltaic power production

A Agga, A Abbou, M Labbadi, Y El Houm… - Electric Power Systems …, 2022 - Elsevier
Climate change is pushing an increasing number of nations to use green energy resources,
particularly solar power as an applicable substitute to traditional power sources. However …

Review and prospect of data-driven techniques for load forecasting in integrated energy systems

J Zhu, H Dong, W Zheng, S Li, Y Huang, L Xi - Applied Energy, 2022 - Elsevier
With synergies among multiple energy sectors, integrated energy systems (IESs) have been
recognized lately as an effective approach to accommodate large-scale renewables and …

Electricity load forecasting: a systematic review

IK Nti, M Teimeh, O Nyarko-Boateng… - Journal of Electrical …, 2020 - Springer
The economic growth of every nation is highly related to its electricity infrastructure, network,
and availability since electricity has become the central part of everyday life in this modern …

Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM

X Qing, Y Niu - Energy, 2018 - Elsevier
Prediction of solar irradiance is essential for minimizing energy costs and providing high
power quality in electrical power grids with distributed solar photovoltaic generations …

Residential load forecasting based on LSTM fusing self-attention mechanism with pooling

H Zang, R Xu, L Cheng, T Ding, L Liu, Z Wei, G Sun - Energy, 2021 - Elsevier
Day-ahead residential load forecasting is crucial for electricity dispatch and demand
response in power systems. Electrical loads are characterized by volatility and uncertainty …

Deep learning framework to forecast electricity demand

J Bedi, D Toshniwal - Applied energy, 2019 - Elsevier
The increasing world population and availability of energy hungry smart devices are major
reasons for alarmingly high electricity consumption in the current times. So far, various …

Model-free real-time EV charging scheduling based on deep reinforcement learning

Z Wan, H Li, H He, D Prokhorov - IEEE Transactions on Smart …, 2018 - ieeexplore.ieee.org
Driven by the recent advances in electric vehicle (EV) technologies, EVs have become
important for smart grid economy. When EVs participate in demand response program which …

Machine learning driven smart electric power systems: Current trends and new perspectives

MS Ibrahim, W Dong, Q Yang - Applied Energy, 2020 - Elsevier
The current power systems are undergoing a rapid transition towards their more active,
flexible, and intelligent counterpart smart grid, which brings about tremendous challenges in …